Dual Attention Feature Fusion for Visible-Infrared Object Detection | |
Hu Yuxuan2,3; Shi Limin4; Yao Libo1; Weng Lubin4 | |
2023-09 | |
会议日期 | 2023-9 |
会议地点 | Heraklion, Greece |
关键词 | Feature fusion Visible-infrared Object detection |
英文摘要 | Feature fusion is an essential component of multimodal object detection to exploit the complementary information and common information between multi-source images. When it comes to visible-infrared image pairs, however, the visible images are prone to illumination and visibility and there may be a lot of interference information and little useful information. We suggest performing common feature enhancement and spatial cross attention sequentially to solve this problem. For this purpose, a novel Dual Attention Transformer Feature Fusion (DATFF) module which is designed for feature fusion of intermediate feature maps is proposed. We integrate it into two-stream object detectors and achieve state-of-the-art performance on DroneVehicle and FLIR visible-infrared object detection datasets. Our code is available at https://github.com/a21401624/DATFF. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/56568] ![]() |
专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
通讯作者 | Weng Lubin |
作者单位 | 1.Institute of Information Fusion, Naval Aviation University, Yantai, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 3.State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China 4.Research Center of Aerospace Information, Institute of Automation, Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Hu Yuxuan,Shi Limin,Yao Libo,et al. Dual Attention Feature Fusion for Visible-Infrared Object Detection[C]. 见:. Heraklion, Greece. 2023-9. |
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